Bridging the "last mile" gap between AI implementation and operation: "data awareness" that matters.

ABSTRACT: Interest in the application of machine learning (ML) techniques to medicine is growing fast and wide because of their ability to endow decision support systems with so-called artificial intelligence, particularly in those medical disciplines that extensively rely on digital imaging. Nonetheless, achieving a pragmatic and ecological validation of medical AI systems in real-world settings is difficult, even when these systems exhibit very high accuracy in laboratory settings. This difficulty has been called the "last mile of implementation." In this review of the concept, we claim that this metaphorical mile presents two chasms: the hiatus of human trust and the hiatus of machine experience. The former hiatus encompasses all that can hinder the concrete use of AI at the point of care, includ

SUBMITTER: Cabitza F 

PROVIDER: S-EPMC7210125 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature